Nanotechnology最新文献

Neuromorphic devices are revolutionizing the field of artificial intelligence (AI) by emulating the neural structure and computational efficiency of the human brain. These devices offer a new computing paradigm that integrates processing and memory, sidestepping the constraints of traditional von Neumann architecture. With capabilities like synaptic plasticity and energy efficiency, neuromorphic devices hold the promise of transforming AI systems into more powerful, adaptive, and efficient platforms. This review focuses on the advanced materials and their applications in neuromorphic devices, such as memristors, ferroelectrics, phase change materials and ionic conductor are at the forefront, enabling the simulation of synaptic weights and the potential for hardware-implemented neural networks. Despite challenges in device uniformity and system-level integration, continuous research and development are pushing the boundaries, aiming to fully realize the potential of neuromorphic computing hardwares.

Material properties of the high-quality MAPbI₃(FAPbI₃) based thin films were optimized using a linear ether (diethyl ether (DE)) and an aromatic hydrocarbon (chlorobenzene (CB)) as antisolvents in order to understand the photovoltaic performance of resultant solar cells. We found that the miscible and immiscible antisolvents influenced the nucleation kinetics and crystal growth of the perovskite thin films, thereby determining the photovoltaic responses. The photovoltaic responses of the MAPbI₃and FAPbI₃based solar cells are better when the miscible CB and immiscible DE are used as the antisolvent, respectively. It is noted that a high power conversion efficiency of 20.8% can be achieved in the FA based mixed perovskite solar cells fabricated with the immiscible DE. The findings from this study should assist in establishing reproducible fabrication processes for various perovskite-related solar cells.

In recent years, atmospheric pollution has been increasing day by day due to the rapid growth of industrialization, urbanization and motor vehicles. As a result, a large number of pollutants containing harmful and toxic gases like NO₂, NH₃, H₂, H₂S and C₃H₆O etc. are being released into the environment. This detrimental increase can cause adverse effects on human life, necessitating monitoring and safety measures. Gas sensors play a crucial role in this regard by monitoring and alerting when pollutant levels exceed permissible limits. Metal oxide semiconductor (MOS) gas sensors have gained much popularity due to their good stability, tunable chemical properties, simple fabrication process and cost effectiveness. SnO₂is the most widely used MOS material for detecting various toxic and hazardous gases, owing to its excellent physical and chemical properties, high reliability and short adsorption and desorption times. In this review, we elevated the role of SnO₂in detecting various toxic and hazardous gases and highlighting different synthesis methods with various structural and morphological modifications are summarized. By reviewing the latest advancements, this paper proposes several future research directions for SnO₂-based gas sensors.

Capillary forces, arising from surface tension and wetting interactions, play a crucial role in nanoscale material behavior, particularly during solvent evaporation in nanowire-based systems. In this study, capillary-induced deformation in silver nanowires is analyzed through a simplified two-step finite element model using ANSYS Static. The effects of these forces during the film formation and moisture treatment stages are investigated for silver nanowires with diameters of 25 nm, 40 nm, and 100 nm. Results show that smaller-diameter nanowires experience significantly higher capillary pressures, leading to greater plastic deformation despite their greater mechanical strength. In contrast, larger wires exhibit lower effective pressure due to reduced capillary efficiency, although the total capillary force is higher. The model contact angle after the film formation step allows for accurate estimation of capillary pressure during the moisture treatment step, enabling the calculation of final strain and stress distributions. Nanometer gaps between wires, which influence cold welding and contact resistance, are also quantitatively analyzed.

This study explores a time-effective, cost-effective, one-pot eco-friendly synthesis method for plant biomolecule-functionalized silver nanoparticles (AgNPs) using aqueous leaf extract ofAverrhoa carambolaL. The polyphenolic compounds naturally present in the leaves act as reducing agents for Ag ions. This environmentally friendly approach eliminates the need of toxic chemicals, external reducing or stabilizing agents, complex instrumentation and specialized technical expertise for a safe, cost-effective and sustainable method for AgNP synthesis in 15 min. The as-synthesized nanoparticles were characterized by transmission electron microscopy, field-emission scanning electron microscopy, x-ray diffraction, and UV-Vis, Fourier transform infrared and Raman spectroscopy. It was observed that the nanoparticles are spherical in shape with an average diameter of 20 nm and have a highly stable zeta potential value of -28.3 mV. The phytochemicals present in the leaf extract were identified by gas chromatography coupled with mass spectrometry. The yellow colloidal AgNP solution with a surface plasmon resonance peak at 416 nm immediately turns to colorless after adding Hg²⁺salt solution over other metal ions. Additionally, the effect of Hg²⁺concentration on AgNP absorption intensity was observed and the limit of detection of mercury was found to be 2.6µM. Therefore, the proposed green route for biomolecule-functionalized AgNPs can be used for rapid detection of hazardous Hg²⁺even at trace concentration in wastewater.

Aluminum sheets are widely used as fins in condensers because of their high thermal conductivity, tractability and affordability. However, the heat transfer efficiency of the aluminum fins was encumbered by the hydrophilicity of the surface where filmwise condensation tends to occur. This work demonstrates the fabrication of a superhydrophobic aluminum surface based on a micro-nano structured hierarchical design using electrochemical wire machining and surface modification technique. By optimizing the applied voltage and the scanning speed of the tool electrode, a superhydrophobic aluminum surface was obtained with a maximum contact angle of 157.2° and a minimum sliding angle of 2.3°. The superhydrophobic aluminum surface demonstrates a dropwise condensation, exhibiting a heat transfer coefficient of 38.2 kW (m²· K)^-1, which is 2.7 times higher than that of the original aluminum sheet. Moreover, the superhydrophobic aluminum surface shows outstanding delayed icing performance, which delays the icing time by 1.9 times compared to the original aluminum sheet under identical conditions. In addition to its superior performance, the proposed electrochemical wire machining method offers significant advantages, including environmental friendliness, mild processing conditions, high efficiency and cost-effectiveness. This work provides a new option for the scalable fabrication of superhydrophobic aluminum fins for high efficiency condensation.

Single event effects (SEE) are a critical reliability concern for Silicon carbide (SiC) MOSFETs, particularly in aerospace applications. While traditional Technology computer-aided design (TCAD) simulations offer accurate SEE prediction, they are computationally intensive and require specialized knowledge. This paper proposes a novel data-driven prediction method with SRIM-TCAD integrated modeling. First, a dataset of 52,920 SEE events in SiC MOSFETs is constructed, considering diverse environment temperatures, heavy ion energies, drain bias voltages, incidence angles, incidence positions, and incidence locations. Then, two different deep learning models are adopted: one to predict the drain transient current pea (I₀) and total collected charge (Q₀), and another to predict the drain transient current pulse. Residual deep neural network (RDNN) is used for predicting theI₀andQ₀. Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) is applied for the drain transient current pulse. A symmetric log-reciprocal data scaling technique is proposed and applied during preprocessing for both models. Experimental results show that the RDNN achieved anR²of 0.99864 forI₀andQ₀prediction, while the CNN-GRU model predicted the drain transient current pulse with anR²of 0.99783. These models provide a prediction speed-up of approximately five to six orders of magnitude compared to TCAD simulations. The proposed method demonstrates high accuracy and significant computational cost reduction, offering an alternative for SEE prediction in SiC MOSFETs and potentially other semiconductor devices.

Optical trapping is a non-invasive technique for manipulating nano- and microscopic objects and is widely used to investigate biological processes, such as membrane viscosity, membrane-cytoskeleton interactions, and the regulation of cellular functions. Optical vortex beams can maintain orbital angular momentum (OAM) and have recently been used for optical manipulation. When nanoparticles in aqueous solutions are rotated at the laser focus owing to the optical forces derived from the optical vortex beam, their subsequent motion is governed by the OAM. The dynamics of nanoparticles attached to the biological membrane may be further affected by the viscoelasticity of the membrane and hydrodynamic coupling; however, it is unclear whether such rotational motion on lipid bilayers can be controlled. In this study, we applied an optical vortex beam to the two-dimensional rotational manipulation of fluorescent nanoparticles attached to a supporting lipid bilayer (SLB) and investigated their rotational behavior. We revealed that the single nanoparticles attached to the SLB rotated more slowly than those in an aqueous solution, but their orbital motion was still clearly driven by the OAM of the beam. The orbital radius of rotation was tuned according to the magnitude of the topological charge, and an angle velocity that changed linearly in proportion to both the laser power and nanoparticle diffusion coefficient was identified, which was consistent with theoretical calculations. These results suggest that optical vortex beams can manipulate nanoparticles attached to SLB with controllable rotational dynamics. Such rotational manipulation of nanoparticles on lipid bilayers can provide a platform for studying the effects of nanoparticle rotation on the local organization of membrane components and can be useful for developing methods to regulate their dynamic properties.

Magnetic skyrmions are nanoscale, topologically protected spin textures that offer exceptional stability, non-volatility, and ultra-low energy manipulation, making them attractive candidates for next-generation computing devices. Their controllable motion in ferromagnet/heavy-metal bilayers enables robust binary state encoding, opening opportunities for energy-efficient decision-making hardware. As adaptive decision-making models, learning automata can benefit from device-level integration, enabling direct in-memory learning with minimal power consumption. This work implements a skyrmion-based learning automata element that maps finite-state transitions to skyrmion motion along nanoscale tracks. The skyrmion's lateral position represents each automaton state ('include' or 'exclude'), and transitions are driven by spin-orbit torque under optimized current densities. This element is demonstrated within the framework of a Tsetlin Machine, providing a hardware-efficient and interpretable logic-learning mechanism. Micromagnetic simulations in MuMax3, utilizing a Co/Pt bilayer, confirm the stable nucleation, motion, and detection of skyrmions. The proposed design achieves 4.86 aJ per state update, representing 99% energy reduction over comparable non-volatile memory-based automata, with a 5ns transition time. This work establishes a scalable and reconfigurable device-level building block for energy-efficient, edge-oriented machine intelligence.

The distinctive physical and chemical characteristics of calcium fluoride (CaF₂), including its cubic crystal structure, wide spectral transmittance, low refractive index, low dispersion, and high chemical stability, make it an essential component in the development of fluorine resources and exhibit indispensable key application values in a variety of industries, including semiconductor, optical, and medical. Systematic summaries of CaF₂'s preparation techniques, characteristics, and devices are still lacking in the academic community, though. The CaF₂summary is the main topic of this paper. It first explains in detail how CaF₂thin films, nano-calcium fluoride, and doped CaF₂are prepared. Additionally, a thorough overview of the application accomplishments of the four categories of electronic devices-energy and metallurgy, biomedicine, and environmental protection-as well as their classifications and combinations available. Finally, it conducts a forward-looking analysis of the future application scenarios and potential challenges of CaF₂, aiming to provide references and impetus for promoting subsequent research on CaF₂, CaF₂.